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How would you handle memory management in Spark?

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Meesho
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
92 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark memory management: (1) Executor memory—split: `spark.memory.fraction` (default 0.6) between execution and storage. (2) Storage—cache uses `spark.memory.storageFraction`. (3) Shuffle—`spark.shuffle.memoryFraction`. (4) Off-heap—enable for large heaps. (5) Avoid OOM: increase partitions, reduce executor memory per task, use `spill` setting....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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